• Data import
• Principles of R
• Data tidying
• Join tables

1. Read sections 11.1 and 11.2
2. Complete the exercises in 11.2.2 in [r4ds.had.co.nz]

• read_csv takes the first 1000 lines of a table to guess the column type.

read_csv("a\na")

## # A tibble: 1 x 1
##   a    
##   <chr>
## 1 a

• read_csv takes the first 1000 lines of a table to guess the column type.

read_csv("a\n1")

## # A tibble: 1 x 1
##       a
##   <int>
## 1     1

• read_csv takes the first 1000 lines of a table to guess the column type.

read_csv("a\n2010-01-01")

## # A tibble: 1 x 1
##   a         
##   <date>    
## 1 2010-01-01

Problems occur if:

• the first 1000 lines are not particularly informative
  • only NA values
  • only integers even though non-integers occur later
• the way in which the values are entered is not entirely clear
  • dates
  • factors

• Binary files
  • feather implements a binary format to share across languages
  • RDS is a format specific to R
• SPSS, SAS, Stata: haven
• Matlab, Excel etc.

df <-
  file %>% 
  read_delim(delim = ",", na = c("", "NA"), 
             skip = 0, ...)

becomes …

df <- 
  file %>% 
  read_csv()

by:

• special functions
• default values

• read_delim has the same interface as read_csv, with additional parameters

1. Each variable forms a column.
2. Each observation forms a row.
3. Each type of observational unit forms a table.

• Data often arrives in an untidy state
• Data tidying refers to the procedure by which we make this data tidy
• Task of the package tidyr (part of the tidyverse)
  • gather summarizes column names as a new column
  • spread spreads variable levels as new column names

table4a

## # A tibble: 3 x 3
##   country     `1999` `2000`
## * <chr>        <int>  <int>
## 1 Afghanistan    745   2666
## 2 Brazil       37737  80488
## 3 China       212258 213766

Fill in the corresponding lines in the code to tidy the table

table4a %>% 
  gather(
    # Specify the name of the key, i. e. the variable which has the 
    # different columns as values
    key = ,
    # What name should the column have in which the former cell values
    # are notated
    value = ,
    # Specify the columns which are variable levels
    ...)

table2

## # A tibble: 12 x 4
##    country      year type            count
##    <chr>       <int> <chr>           <int>
##  1 Afghanistan  1999 cases             745
##  2 Afghanistan  1999 population   19987071
##  3 Afghanistan  2000 cases            2666
##  4 Afghanistan  2000 population   20595360
##  5 Brazil       1999 cases           37737
##  6 Brazil       1999 population  172006362
##  7 Brazil       2000 cases           80488
##  8 Brazil       2000 population  174504898
##  9 China        1999 cases          212258
## 10 China        1999 population 1272915272
## 11 China        2000 cases          213766
## 12 China        2000 population 1280428583

Fill in the corresponding lines in the code to tidy the table

table2 %>% 
  spread(
    # the variable which gives the new column names
    key = ,
    # the variable which gives the new column cell values
    value = 
  )

• combine information from two data frames
• covered by the *_join functions in dplyr
• join by certain columns

• combine information from two data frames
• covered by the *_join functions in dplyr
• join by certain columns

table1 %>% 
  *_join(table2, by = "column(s)")

band_instruments

## # A tibble: 3 x 2
##   name  plays 
##   <chr> <chr> 
## 1 John  guitar
## 2 Paul  bass  
## 3 Keith guitar

band_members

## # A tibble: 3 x 2
##   name  band   
##   <chr> <chr>  
## 1 Mick  Stones 
## 2 John  Beatles
## 3 Paul  Beatles

• generate a table with columns from both tables
• differ in the observations which they keep

1. Generate a table with all band members from band_members and the instruments which they are playing.
2. Generate a table which gives you information all players where you know in which band they are playing which instrument

• filter table1
• semi_join keeps the observation which can be found in table2
• anti_join keeps all other observations

1. Generate a table with all band members whose instruments is not known

• R4DS, ch. 11-13
• Data transformation cheat sheet
• Data import cheat sheet (includes tidyr functions)
• Animated explanations of R functions